Overview

Dataset statistics

Number of variables17
Number of observations68672
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Text4
Categorical4

Alerts

END_SLK is highly overall correlated with END_TRUE_DIST and 1 other fieldsHigh correlation
END_TRUE_DIST is highly overall correlated with END_SLK and 1 other fieldsHigh correlation
GEOLOCSTLength is highly overall correlated with END_SLK and 1 other fieldsHigh correlation
LG_NO is highly overall correlated with RA_NAMEHigh correlation
NETWORK_TYPE is highly overall correlated with SPEED_LIMIT and 2 other fieldsHigh correlation
RA_NAME is highly overall correlated with LG_NOHigh correlation
SPEED_LIMIT is highly overall correlated with NETWORK_TYPEHigh correlation
START_SLK is highly overall correlated with NETWORK_TYPE and 1 other fieldsHigh correlation
START_TRUE_DIST is highly overall correlated with NETWORK_TYPE and 1 other fieldsHigh correlation
CWY is highly imbalanced (85.2%)Imbalance
NETWORK_TYPE is highly imbalanced (70.5%)Imbalance
SPEED_LIMIT is highly imbalanced (64.1%)Imbalance
START_SLK is highly skewed (γ1 = 24.64322751)Skewed
END_SLK is highly skewed (γ1 = 24.06224455)Skewed
START_TRUE_DIST is highly skewed (γ1 = 24.73083934)Skewed
END_TRUE_DIST is highly skewed (γ1 = 24.14762603)Skewed
OBJECTID is uniformly distributedUniform
OBJECTID has unique valuesUnique
START_SLK has 59959 (87.3%) zerosZeros
START_TRUE_DIST has 60005 (87.4%) zerosZeros

Reproduction

Analysis started2023-12-12 15:07:15.757789
Analysis finished2023-12-12 15:07:24.339059
Duration8.58 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct68672
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84193446
Minimum84159111
Maximum84227782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:24.382526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum84159111
5-th percentile84162545
Q184176279
median84193446
Q384210614
95-th percentile84224348
Maximum84227782
Range68671
Interquartile range (IQR)34335.5

Descriptive statistics

Standard deviation19824.043
Coefficient of variation (CV)0.00023545827
Kurtosis-1.2
Mean84193446
Median Absolute Deviation (MAD)17168
Skewness0
Sum5.7817324 × 1012
Variance3.9299269 × 108
MonotonicityStrictly increasing
2023-12-12T10:07:24.540798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84159111 1
 
< 0.1%
84204898 1
 
< 0.1%
84204884 1
 
< 0.1%
84204885 1
 
< 0.1%
84204886 1
 
< 0.1%
84204887 1
 
< 0.1%
84204888 1
 
< 0.1%
84204889 1
 
< 0.1%
84204890 1
 
< 0.1%
84204891 1
 
< 0.1%
Other values (68662) 68662
> 99.9%
ValueCountFrequency (%)
84159111 1
< 0.1%
84159112 1
< 0.1%
84159113 1
< 0.1%
84159114 1
< 0.1%
84159115 1
< 0.1%
84159116 1
< 0.1%
84159117 1
< 0.1%
84159118 1
< 0.1%
84159119 1
< 0.1%
84159120 1
< 0.1%
ValueCountFrequency (%)
84227782 1
< 0.1%
84227781 1
< 0.1%
84227780 1
< 0.1%
84227779 1
< 0.1%
84227778 1
< 0.1%
84227777 1
< 0.1%
84227776 1
< 0.1%
84227775 1
< 0.1%
84227774 1
< 0.1%
84227773 1
< 0.1%

ROAD
Text

Distinct59783
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:24.726071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8438228
Min length4

Characters and Unicode

Total characters469979
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55604 ?
Unique (%)81.0%

Sample

1st row2110042
2nd row2040155
3rd row2040149
4th row2040145
5th row2040144
ValueCountFrequency (%)
h009 200
 
0.3%
h006 177
 
0.3%
h005 152
 
0.2%
m031 104
 
0.2%
h001 99
 
0.1%
h043 84
 
0.1%
h002 70
 
0.1%
h017 70
 
0.1%
h007 67
 
0.1%
m037 61
 
0.1%
Other values (59773) 67588
98.4%
2023-12-12T10:07:24.980100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 119875
25.5%
1 103138
21.9%
2 50670
10.8%
3 37713
 
8.0%
4 33496
 
7.1%
5 33086
 
7.0%
6 24044
 
5.1%
7 22233
 
4.7%
8 21374
 
4.5%
9 20775
 
4.4%
Other values (2) 3575
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 466404
99.2%
Uppercase Letter 3575
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119875
25.7%
1 103138
22.1%
2 50670
10.9%
3 37713
 
8.1%
4 33496
 
7.2%
5 33086
 
7.1%
6 24044
 
5.2%
7 22233
 
4.8%
8 21374
 
4.6%
9 20775
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
H 2587
72.4%
M 988
 
27.6%

Most occurring scripts

ValueCountFrequency (%)
Common 466404
99.2%
Latin 3575
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119875
25.7%
1 103138
22.1%
2 50670
10.9%
3 37713
 
8.1%
4 33496
 
7.2%
5 33086
 
7.1%
6 24044
 
5.2%
7 22233
 
4.8%
8 21374
 
4.6%
9 20775
 
4.5%
Latin
ValueCountFrequency (%)
H 2587
72.4%
M 988
 
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119875
25.5%
1 103138
21.9%
2 50670
10.8%
3 37713
 
8.0%
4 33496
 
7.1%
5 33086
 
7.0%
6 24044
 
5.1%
7 22233
 
4.7%
8 21374
 
4.5%
9 20775
 
4.4%
Other values (2) 3575
 
0.8%
Distinct45773
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:25.179115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length69
Median length66
Mean length11.256407
Min length4

Characters and Unicode

Total characters773000
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36897 ?
Unique (%)53.7%

Sample

1st rowHacket Rd
2nd rowKnight St
3rd rowTimperley Rd
4th rowMossop St
5th rowGuthrie St
ValueCountFrequency (%)
rd 20173
 
13.2%
st 13180
 
8.6%
wy 4690
 
3.1%
pl 4374
 
2.9%
ct 3806
 
2.5%
dr 2604
 
1.7%
l 2526
 
1.7%
av 2365
 
1.5%
hwy 1954
 
1.3%
cl 1756
 
1.2%
Other values (25126) 95163
62.4%
2023-12-12T10:07:25.440522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83942
 
10.9%
e 53734
 
7.0%
a 50182
 
6.5%
r 47445
 
6.1%
t 44287
 
5.7%
n 42499
 
5.5%
o 41845
 
5.4%
l 39297
 
5.1%
d 38049
 
4.9%
i 32188
 
4.2%
Other values (63) 299532
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 531196
68.7%
Uppercase Letter 151059
 
19.5%
Space Separator 83942
 
10.9%
Decimal Number 1851
 
0.2%
Other Punctuation 1500
 
0.2%
Dash Punctuation 1183
 
0.2%
Open Punctuation 1135
 
0.1%
Close Punctuation 1133
 
0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53734
10.1%
a 50182
9.4%
r 47445
 
8.9%
t 44287
 
8.3%
n 42499
 
8.0%
o 41845
 
7.9%
l 39297
 
7.4%
d 38049
 
7.2%
i 32188
 
6.1%
s 22580
 
4.3%
Other values (16) 119090
22.4%
Uppercase Letter
ValueCountFrequency (%)
R 25470
16.9%
S 20345
13.5%
C 15217
10.1%
W 10298
 
6.8%
P 9450
 
6.3%
B 8266
 
5.5%
L 7426
 
4.9%
M 7244
 
4.8%
H 6324
 
4.2%
A 6279
 
4.2%
Other values (16) 34740
23.0%
Decimal Number
ValueCountFrequency (%)
1 288
15.6%
4 287
15.5%
3 267
14.4%
2 179
9.7%
7 155
8.4%
5 153
8.3%
0 146
7.9%
8 144
7.8%
6 118
6.4%
9 114
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 1307
87.1%
' 148
 
9.9%
& 22
 
1.5%
/ 13
 
0.9%
: 7
 
0.5%
# 3
 
0.2%
Space Separator
ValueCountFrequency (%)
83942
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1133
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 682255
88.3%
Common 90745
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53734
 
7.9%
a 50182
 
7.4%
r 47445
 
7.0%
t 44287
 
6.5%
n 42499
 
6.2%
o 41845
 
6.1%
l 39297
 
5.8%
d 38049
 
5.6%
i 32188
 
4.7%
R 25470
 
3.7%
Other values (42) 267259
39.2%
Common
ValueCountFrequency (%)
83942
92.5%
. 1307
 
1.4%
- 1183
 
1.3%
( 1135
 
1.3%
) 1133
 
1.2%
1 288
 
0.3%
4 287
 
0.3%
3 267
 
0.3%
2 179
 
0.2%
7 155
 
0.2%
Other values (11) 869
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 773000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83942
 
10.9%
e 53734
 
7.0%
a 50182
 
6.5%
r 47445
 
6.1%
t 44287
 
5.7%
n 42499
 
5.5%
o 41845
 
5.4%
l 39297
 
5.1%
d 38049
 
4.9%
i 32188
 
4.2%
Other values (63) 299532
38.7%
Distinct45839
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:25.648627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length69
Median length66
Mean length11.230341
Min length4

Characters and Unicode

Total characters771210
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36906 ?
Unique (%)53.7%

Sample

1st rowHacket Rd
2nd rowKnight St
3rd rowTimperley Rd
4th rowMossop St
5th rowGuthrie St
ValueCountFrequency (%)
rd 20974
 
13.7%
st 13395
 
8.8%
wy 4686
 
3.1%
pl 4377
 
2.9%
ct 3806
 
2.5%
dr 2650
 
1.7%
l 2526
 
1.7%
av 2395
 
1.6%
cl 1756
 
1.1%
hwy 1729
 
1.1%
Other values (25147) 94564
61.9%
2023-12-12T10:07:25.917770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84209
 
10.9%
e 53400
 
6.9%
a 49865
 
6.5%
r 46951
 
6.1%
t 44463
 
5.8%
n 42313
 
5.5%
o 41229
 
5.3%
l 38810
 
5.0%
d 38732
 
5.0%
i 32025
 
4.2%
Other values (63) 299213
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 528736
68.6%
Uppercase Letter 151369
 
19.6%
Space Separator 84209
 
10.9%
Decimal Number 1888
 
0.2%
Other Punctuation 1529
 
0.2%
Dash Punctuation 1220
 
0.2%
Open Punctuation 1130
 
0.1%
Close Punctuation 1128
 
0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53400
10.1%
a 49865
9.4%
r 46951
 
8.9%
t 44463
 
8.4%
n 42313
 
8.0%
o 41229
 
7.8%
l 38810
 
7.3%
d 38732
 
7.3%
i 32025
 
6.1%
s 22711
 
4.3%
Other values (16) 118237
22.4%
Uppercase Letter
ValueCountFrequency (%)
R 26319
17.4%
S 20633
13.6%
C 15100
10.0%
W 10295
 
6.8%
P 9484
 
6.3%
B 8231
 
5.4%
L 7277
 
4.8%
M 7007
 
4.6%
A 6282
 
4.2%
H 6128
 
4.0%
Other values (16) 34613
22.9%
Decimal Number
ValueCountFrequency (%)
1 296
15.7%
4 287
15.2%
3 277
14.7%
2 180
9.5%
0 159
8.4%
5 156
8.3%
7 153
8.1%
8 145
7.7%
6 121
6.4%
9 114
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 1331
87.1%
' 149
 
9.7%
& 23
 
1.5%
/ 16
 
1.0%
: 7
 
0.5%
# 3
 
0.2%
Space Separator
ValueCountFrequency (%)
84209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1130
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1128
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 680105
88.2%
Common 91105
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53400
 
7.9%
a 49865
 
7.3%
r 46951
 
6.9%
t 44463
 
6.5%
n 42313
 
6.2%
o 41229
 
6.1%
l 38810
 
5.7%
d 38732
 
5.7%
i 32025
 
4.7%
R 26319
 
3.9%
Other values (42) 265998
39.1%
Common
ValueCountFrequency (%)
84209
92.4%
. 1331
 
1.5%
- 1220
 
1.3%
( 1130
 
1.2%
) 1128
 
1.2%
1 296
 
0.3%
4 287
 
0.3%
3 277
 
0.3%
2 180
 
0.2%
0 159
 
0.2%
Other values (11) 888
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 771210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84209
 
10.9%
e 53400
 
6.9%
a 49865
 
6.5%
r 46951
 
6.1%
t 44463
 
5.8%
n 42313
 
5.5%
o 41229
 
5.3%
l 38810
 
5.0%
d 38732
 
5.0%
i 32025
 
4.2%
Other values (63) 299213
38.8%

START_SLK
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3571
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6446895
Minimum0
Maximum3194.2
Zeros59959
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:25.996855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.83
Maximum3194.2
Range3194.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation96.190029
Coefficient of variation (CV)12.582595
Kurtosis701.92989
Mean7.6446895
Median Absolute Deviation (MAD)0
Skewness24.643228
Sum524976.12
Variance9252.5217
MonotonicityNot monotonic
2023-12-12T10:07:26.049485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59959
87.3%
0.09 110
 
0.2%
0.1 96
 
0.1%
0.08 84
 
0.1%
0.03 80
 
0.1%
0.02 75
 
0.1%
0.06 70
 
0.1%
0.07 65
 
0.1%
0.05 64
 
0.1%
0.04 61
 
0.1%
Other values (3561) 8008
 
11.7%
ValueCountFrequency (%)
0 59959
87.3%
0.006 1
 
< 0.1%
0.01 35
 
0.1%
0.011 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.015 1
 
< 0.1%
0.02 75
 
0.1%
0.022 1
 
< 0.1%
0.023 1
 
< 0.1%
ValueCountFrequency (%)
3194.2 1
< 0.1%
3194.18 1
< 0.1%
3193.98 1
< 0.1%
3193.69 1
< 0.1%
3193.45 1
< 0.1%
3189.86 1
< 0.1%
3189.34 1
< 0.1%
3188.17 1
< 0.1%
3188.03 1
< 0.1%
3186.18 1
< 0.1%

END_SLK
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5875
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8074911
Minimum0.01
Maximum3194.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.100774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q10.16
median0.366
Q31.26
95-th percentile19.8335
Maximum3194.66
Range3194.65
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation97.663996
Coefficient of variation (CV)9.9581019
Kurtosis675.2453
Mean9.8074911
Median Absolute Deviation (MAD)0.266
Skewness24.062245
Sum673500.03
Variance9538.2561
MonotonicityNot monotonic
2023-12-12T10:07:26.154094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 1764
 
2.6%
0.1 1564
 
2.3%
0.09 1468
 
2.1%
0.12 1328
 
1.9%
0.11 1293
 
1.9%
0.16 1252
 
1.8%
0.13 1170
 
1.7%
0.15 1157
 
1.7%
0.14 1137
 
1.7%
0.07 1123
 
1.6%
Other values (5865) 55416
80.7%
ValueCountFrequency (%)
0.01 46
 
0.1%
0.011 1
 
< 0.1%
0.013 2
 
< 0.1%
0.015 3
 
< 0.1%
0.017 2
 
< 0.1%
0.018 2
 
< 0.1%
0.019 1
 
< 0.1%
0.02 153
0.2%
0.022 2
 
< 0.1%
0.023 1
 
< 0.1%
ValueCountFrequency (%)
3194.66 1
< 0.1%
3194.2 1
< 0.1%
3194.18 1
< 0.1%
3193.98 1
< 0.1%
3193.69 1
< 0.1%
3193.45 1
< 0.1%
3189.86 1
< 0.1%
3189.04 1
< 0.1%
3188.28 1
< 0.1%
3188.03 1
< 0.1%

CWY
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
Single
66502 
Left
 
1098
Right
 
1072

Length

Max length6
Median length6
Mean length5.9524115
Min length4

Characters and Unicode

Total characters408764
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 66502
96.8%
Left 1098
 
1.6%
Right 1072
 
1.6%

Length

2023-12-12T10:07:26.206741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:07:26.255038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
single 66502
96.8%
left 1098
 
1.6%
right 1072
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 67600
16.5%
i 67574
16.5%
g 67574
16.5%
S 66502
16.3%
n 66502
16.3%
l 66502
16.3%
t 2170
 
0.5%
L 1098
 
0.3%
f 1098
 
0.3%
R 1072
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 340092
83.2%
Uppercase Letter 68672
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 67600
19.9%
i 67574
19.9%
g 67574
19.9%
n 66502
19.6%
l 66502
19.6%
t 2170
 
0.6%
f 1098
 
0.3%
h 1072
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 66502
96.8%
L 1098
 
1.6%
R 1072
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 408764
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 67600
16.5%
i 67574
16.5%
g 67574
16.5%
S 66502
16.3%
n 66502
16.3%
l 66502
16.3%
t 2170
 
0.5%
L 1098
 
0.3%
f 1098
 
0.3%
R 1072
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 67600
16.5%
i 67574
16.5%
g 67574
16.5%
S 66502
16.3%
n 66502
16.3%
l 66502
16.3%
t 2170
 
0.5%
L 1098
 
0.3%
f 1098
 
0.3%
R 1072
 
0.3%

START_TRUE_DIST
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3549
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5768295
Minimum0
Maximum3194.55
Zeros60005
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.298718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.61
Maximum3194.55
Range3194.55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation96.104057
Coefficient of variation (CV)12.683941
Kurtosis705.91581
Mean7.5768295
Median Absolute Deviation (MAD)0
Skewness24.730839
Sum520316.03
Variance9235.9898
MonotonicityNot monotonic
2023-12-12T10:07:26.352120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60005
87.4%
0.09 110
 
0.2%
0.1 96
 
0.1%
0.08 84
 
0.1%
0.03 78
 
0.1%
0.02 73
 
0.1%
0.06 69
 
0.1%
0.05 65
 
0.1%
0.07 65
 
0.1%
0.17 62
 
0.1%
Other values (3539) 7965
 
11.6%
ValueCountFrequency (%)
0 60005
87.4%
0.01 32
 
< 0.1%
0.011 1
 
< 0.1%
0.012 2
 
< 0.1%
0.013 1
 
< 0.1%
0.015 1
 
< 0.1%
0.02 73
 
0.1%
0.022 1
 
< 0.1%
0.023 1
 
< 0.1%
0.024 2
 
< 0.1%
ValueCountFrequency (%)
3194.55 1
< 0.1%
3194.53 1
< 0.1%
3194.33 1
< 0.1%
3194.04 1
< 0.1%
3193.8 1
< 0.1%
3190.21 1
< 0.1%
3189.69 1
< 0.1%
3188.82 1
< 0.1%
3188.57 1
< 0.1%
3186.72 1
< 0.1%

END_TRUE_DIST
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5837
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.739631
Minimum0.01
Maximum3195.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.403664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.06955
Q10.16
median0.365
Q31.25
95-th percentile19.609
Maximum3195.01
Range3195
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation97.577041
Coefficient of variation (CV)10.018556
Kurtosis679.10263
Mean9.739631
Median Absolute Deviation (MAD)0.265
Skewness24.147626
Sum668839.94
Variance9521.2789
MonotonicityNot monotonic
2023-12-12T10:07:26.459719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 1765
 
2.6%
0.1 1563
 
2.3%
0.09 1468
 
2.1%
0.12 1330
 
1.9%
0.11 1293
 
1.9%
0.16 1252
 
1.8%
0.13 1169
 
1.7%
0.15 1157
 
1.7%
0.14 1137
 
1.7%
0.07 1123
 
1.6%
Other values (5827) 55415
80.7%
ValueCountFrequency (%)
0.01 46
 
0.1%
0.011 1
 
< 0.1%
0.013 2
 
< 0.1%
0.015 3
 
< 0.1%
0.017 2
 
< 0.1%
0.018 2
 
< 0.1%
0.019 1
 
< 0.1%
0.02 153
0.2%
0.022 2
 
< 0.1%
0.023 1
 
< 0.1%
ValueCountFrequency (%)
3195.01 1
< 0.1%
3194.55 1
< 0.1%
3194.53 1
< 0.1%
3194.33 1
< 0.1%
3194.04 1
< 0.1%
3193.8 1
< 0.1%
3190.21 1
< 0.1%
3189.69 1
< 0.1%
3188.82 1
< 0.1%
3188.57 1
< 0.1%

NETWORK_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
Local Road
65097 
State Road
 
3575

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters686720
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLocal Road
2nd rowLocal Road
3rd rowLocal Road
4th rowLocal Road
5th rowLocal Road

Common Values

ValueCountFrequency (%)
Local Road 65097
94.8%
State Road 3575
 
5.2%

Length

2023-12-12T10:07:26.505268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:07:26.540605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
road 68672
50.0%
local 65097
47.4%
state 3575
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 137344
20.0%
o 133769
19.5%
68672
10.0%
R 68672
10.0%
d 68672
10.0%
L 65097
9.5%
c 65097
9.5%
l 65097
9.5%
t 7150
 
1.0%
S 3575
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 480704
70.0%
Uppercase Letter 137344
 
20.0%
Space Separator 68672
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 137344
28.6%
o 133769
27.8%
d 68672
14.3%
c 65097
13.5%
l 65097
13.5%
t 7150
 
1.5%
e 3575
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
R 68672
50.0%
L 65097
47.4%
S 3575
 
2.6%
Space Separator
ValueCountFrequency (%)
68672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 618048
90.0%
Common 68672
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 137344
22.2%
o 133769
21.6%
R 68672
11.1%
d 68672
11.1%
L 65097
10.5%
c 65097
10.5%
l 65097
10.5%
t 7150
 
1.2%
S 3575
 
0.6%
e 3575
 
0.6%
Common
ValueCountFrequency (%)
68672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 137344
20.0%
o 133769
19.5%
68672
10.0%
R 68672
10.0%
d 68672
10.0%
L 65097
9.5%
c 65097
9.5%
l 65097
9.5%
t 7150
 
1.0%
S 3575
 
0.5%

RA_NO
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4395969
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.571252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median7
Q37
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0114784
Coefficient of variation (CV)0.46765014
Kurtosis0.74995767
Mean6.4395969
Median Absolute Deviation (MAD)0
Skewness0.17102201
Sum442220
Variance9.0690022
MonotonicityNot monotonic
2023-12-12T10:07:26.611566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 36010
52.4%
2 10418
 
15.2%
8 9347
 
13.6%
1 4056
 
5.9%
14 3914
 
5.7%
5 2241
 
3.3%
11 1673
 
2.4%
6 1013
 
1.5%
ValueCountFrequency (%)
1 4056
 
5.9%
2 10418
 
15.2%
5 2241
 
3.3%
6 1013
 
1.5%
7 36010
52.4%
8 9347
 
13.6%
11 1673
 
2.4%
14 3914
 
5.7%
ValueCountFrequency (%)
14 3914
 
5.7%
11 1673
 
2.4%
8 9347
 
13.6%
7 36010
52.4%
6 1013
 
1.5%
5 2241
 
3.3%
2 10418
 
15.2%
1 4056
 
5.9%

RA_NAME
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
Metropolitan
36010 
South West
10418 
Wheatbelt
9347 
Great Southern
4056 
Mid West-Gascoyne
3914 
Other values (3)
4927 

Length

Max length22
Median length12
Mean length11.851628
Min length7

Characters and Unicode

Total characters813875
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth West
2nd rowSouth West
3rd rowSouth West
4th rowSouth West
5th rowSouth West

Common Values

ValueCountFrequency (%)
Metropolitan 36010
52.4%
South West 10418
 
15.2%
Wheatbelt 9347
 
13.6%
Great Southern 4056
 
5.9%
Mid West-Gascoyne 3914
 
5.7%
Goldfields - Esperance 2241
 
3.3%
Pilbara 1673
 
2.4%
Kimberley 1013
 
1.5%

Length

2023-12-12T10:07:26.663462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:07:26.711680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan 36010
39.3%
south 10418
 
11.4%
west 10418
 
11.4%
wheatbelt 9347
 
10.2%
great 4056
 
4.4%
southern 4056
 
4.4%
mid 3914
 
4.3%
west-gascoyne 3914
 
4.3%
goldfields 2241
 
2.4%
2241
 
2.4%
Other values (3) 4927
 
5.4%

Most occurring characters

ValueCountFrequency (%)
t 123576
15.2%
o 92649
11.4%
e 89811
11.0%
a 58914
 
7.2%
l 52525
 
6.5%
r 49049
 
6.0%
n 46221
 
5.7%
i 44851
 
5.5%
M 39924
 
4.9%
p 38251
 
4.7%
Other values (17) 178104
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 691635
85.0%
Uppercase Letter 93215
 
11.5%
Space Separator 22870
 
2.8%
Dash Punctuation 6155
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 123576
17.9%
o 92649
13.4%
e 89811
13.0%
a 58914
8.5%
l 52525
7.6%
r 49049
 
7.1%
n 46221
 
6.7%
i 44851
 
6.5%
p 38251
 
5.5%
h 23821
 
3.4%
Other values (8) 71967
10.4%
Uppercase Letter
ValueCountFrequency (%)
M 39924
42.8%
W 23679
25.4%
S 14474
 
15.5%
G 10211
 
11.0%
E 2241
 
2.4%
P 1673
 
1.8%
K 1013
 
1.1%
Space Separator
ValueCountFrequency (%)
22870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 784850
96.4%
Common 29025
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 123576
15.7%
o 92649
11.8%
e 89811
11.4%
a 58914
 
7.5%
l 52525
 
6.7%
r 49049
 
6.2%
n 46221
 
5.9%
i 44851
 
5.7%
M 39924
 
5.1%
p 38251
 
4.9%
Other values (15) 149079
19.0%
Common
ValueCountFrequency (%)
22870
78.8%
- 6155
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 813875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 123576
15.2%
o 92649
11.4%
e 89811
11.0%
a 58914
 
7.2%
l 52525
 
6.5%
r 49049
 
6.0%
n 46221
 
5.7%
i 44851
 
5.5%
M 39924
 
4.9%
p 38251
 
4.7%
Other values (17) 178104
21.9%

LG_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.42469
Minimum1
Maximum814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.767990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102
Q1109
median131
Q3315
95-th percentile608
Maximum814
Range813
Interquartile range (IQR)206

Descriptive statistics

Standard deviation187.76893
Coefficient of variation (CV)0.77775365
Kurtosis1.1317248
Mean241.42469
Median Absolute Deviation (MAD)30
Skewness1.3950741
Sum16579116
Variance35257.171
MonotonicityNot monotonic
2023-12-12T10:07:26.824549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 4473
 
6.5%
109 3207
 
4.7%
131 3194
 
4.7%
107 2746
 
4.0%
125 2671
 
3.9%
103 2523
 
3.7%
104 2150
 
3.1%
212 2126
 
3.1%
101 1869
 
2.7%
114 1534
 
2.2%
Other values (129) 42179
61.4%
ValueCountFrequency (%)
1 380
 
0.6%
2 89
 
0.1%
3 209
 
0.3%
4 336
 
0.5%
101 1869
2.7%
102 1271
1.9%
103 2523
3.7%
104 2150
3.1%
105 1073
1.6%
106 1010
1.5%
ValueCountFrequency (%)
814 595
0.9%
813 535
0.8%
812 261
0.4%
811 279
0.4%
806 162
 
0.2%
805 53
 
0.1%
804 90
 
0.1%
803 293
0.4%
707 77
 
0.1%
706 65
 
0.1%
Distinct139
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:26.985027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length11.46409
Min length3

Characters and Unicode

Total characters787262
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHarvey
2nd rowBunbury (C)
3rd rowBunbury (C)
4th rowBunbury (C)
5th rowBunbury (C)
ValueCountFrequency (%)
c 39394
31.6%
wanneroo 4473
 
3.6%
3980
 
3.2%
swan 3207
 
2.6%
joondalup 3194
 
2.6%
rockingham 2746
 
2.2%
stirling 2671
 
2.1%
cockburn 2523
 
2.0%
gosnells 2150
 
1.7%
mandurah 2126
 
1.7%
Other values (155) 58034
46.6%
2023-12-12T10:07:27.219748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68927
 
8.8%
n 68765
 
8.7%
55826
 
7.1%
r 50452
 
6.4%
C 47393
 
6.0%
e 46243
 
5.9%
o 44992
 
5.7%
( 41435
 
5.3%
) 41435
 
5.3%
l 36324
 
4.6%
Other values (41) 285470
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 524068
66.6%
Uppercase Letter 120518
 
15.3%
Space Separator 55826
 
7.1%
Open Punctuation 41435
 
5.3%
Close Punctuation 41435
 
5.3%
Dash Punctuation 3980
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68927
13.2%
n 68765
13.1%
r 50452
9.6%
e 46243
8.8%
o 44992
 
8.6%
l 36324
 
6.9%
i 29927
 
5.7%
u 24196
 
4.6%
t 22063
 
4.2%
d 18730
 
3.6%
Other values (14) 113449
21.6%
Uppercase Letter
ValueCountFrequency (%)
C 47393
39.3%
M 8522
 
7.1%
S 7826
 
6.5%
B 7780
 
6.5%
W 6952
 
5.8%
G 6073
 
5.0%
K 5631
 
4.7%
A 4373
 
3.6%
J 4249
 
3.5%
R 4065
 
3.4%
Other values (13) 17654
 
14.6%
Space Separator
ValueCountFrequency (%)
55826
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41435
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41435
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 644586
81.9%
Common 142676
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68927
 
10.7%
n 68765
 
10.7%
r 50452
 
7.8%
C 47393
 
7.4%
e 46243
 
7.2%
o 44992
 
7.0%
l 36324
 
5.6%
i 29927
 
4.6%
u 24196
 
3.8%
t 22063
 
3.4%
Other values (37) 205304
31.9%
Common
ValueCountFrequency (%)
55826
39.1%
( 41435
29.0%
) 41435
29.0%
- 3980
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 787262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68927
 
8.8%
n 68765
 
8.7%
55826
 
7.1%
r 50452
 
6.4%
C 47393
 
6.0%
e 46243
 
5.9%
o 44992
 
5.7%
( 41435
 
5.3%
) 41435
 
5.3%
l 36324
 
4.6%
Other values (41) 285470
36.3%

SPEED_LIMIT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size536.6 KiB
50km/h applies in built up areas or 110km/h outside built up areas
54087 
50km/h
6028 
60km/h
 
2397
70km/h
 
1676
110km/h
 
1368
Other values (7)
 
3116

Length

Max length66
Median length66
Mean length53.283172
Min length6

Characters and Unicode

Total characters3659062
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50km/h applies in built up areas or 110km/h outside built up areas
2nd row50km/h applies in built up areas or 110km/h outside built up areas
3rd row50km/h applies in built up areas or 110km/h outside built up areas
4th row50km/h applies in built up areas or 110km/h outside built up areas
5th row50km/h applies in built up areas or 110km/h outside built up areas

Common Values

ValueCountFrequency (%)
50km/h applies in built up areas or 110km/h outside built up areas 54087
78.8%
50km/h 6028
 
8.8%
60km/h 2397
 
3.5%
70km/h 1676
 
2.4%
110km/h 1368
 
2.0%
80km/h 1328
 
1.9%
40km/h 708
 
1.0%
90km/h 504
 
0.7%
100km/h 442
 
0.6%
30km/h 73
 
0.1%
Other values (2) 61
 
0.1%

Length

2023-12-12T10:07:27.300599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
built 108174
16.3%
up 108174
16.3%
areas 108174
16.3%
50km/h 60115
9.1%
110km/h 55455
8.4%
applies 54087
8.2%
in 54087
8.2%
or 54087
8.2%
outside 54087
8.2%
60km/h 2397
 
0.4%
Other values (8) 4792
 
0.7%

Most occurring characters

ValueCountFrequency (%)
594957
16.3%
i 270435
 
7.4%
u 270435
 
7.4%
a 270435
 
7.4%
s 216348
 
5.9%
e 216348
 
5.9%
p 216348
 
5.9%
l 162261
 
4.4%
t 162261
 
4.4%
r 162261
 
4.4%
Other values (18) 1116973
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2639931
72.1%
Space Separator 594957
 
16.3%
Decimal Number 301415
 
8.2%
Other Punctuation 122759
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 270435
10.2%
u 270435
10.2%
a 270435
10.2%
s 216348
 
8.2%
e 216348
 
8.2%
p 216348
 
8.2%
l 162261
 
6.1%
t 162261
 
6.1%
r 162261
 
6.1%
h 122759
 
4.7%
Other values (6) 570040
21.6%
Decimal Number
ValueCountFrequency (%)
0 123201
40.9%
1 111404
37.0%
5 60115
19.9%
6 2397
 
0.8%
7 1676
 
0.6%
8 1328
 
0.4%
4 708
 
0.2%
9 504
 
0.2%
3 73
 
< 0.1%
2 9
 
< 0.1%
Space Separator
ValueCountFrequency (%)
594957
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 122759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2639931
72.1%
Common 1019131
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 270435
10.2%
u 270435
10.2%
a 270435
10.2%
s 216348
 
8.2%
e 216348
 
8.2%
p 216348
 
8.2%
l 162261
 
6.1%
t 162261
 
6.1%
r 162261
 
6.1%
h 122759
 
4.7%
Other values (6) 570040
21.6%
Common
ValueCountFrequency (%)
594957
58.4%
0 123201
 
12.1%
/ 122759
 
12.0%
1 111404
 
10.9%
5 60115
 
5.9%
6 2397
 
0.2%
7 1676
 
0.2%
8 1328
 
0.1%
4 708
 
0.1%
9 504
 
< 0.1%
Other values (2) 82
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3659062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
594957
16.3%
i 270435
 
7.4%
u 270435
 
7.4%
a 270435
 
7.4%
s 216348
 
5.9%
e 216348
 
5.9%
p 216348
 
5.9%
l 162261
 
4.4%
t 162261
 
4.4%
r 162261
 
4.4%
Other values (18) 1116973
30.5%

ROUTE_NE_ID
Real number (ℝ)

Distinct59783
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36249134
Minimum141746
Maximum6.0812068 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:27.347424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum141746
5-th percentile203327.55
Q1216424
median232832.5
Q3247800
95-th percentile2.9783466 × 108
Maximum6.0812068 × 108
Range6.0797893 × 108
Interquartile range (IQR)31376

Descriptive statistics

Standard deviation1.0412624 × 108
Coefficient of variation (CV)2.8725167
Kurtosis11.52105
Mean36249134
Median Absolute Deviation (MAD)15327
Skewness3.4744386
Sum2.4893005 × 1012
Variance1.0842274 × 1016
MonotonicityNot monotonic
2023-12-12T10:07:27.401266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247846 200
 
0.3%
247653 177
 
0.3%
247651 152
 
0.2%
247804 104
 
0.2%
247561 99
 
0.1%
247595 84
 
0.1%
247815 70
 
0.1%
247857 70
 
0.1%
247803 67
 
0.1%
247616 61
 
0.1%
Other values (59773) 67588
98.4%
ValueCountFrequency (%)
141746 1
< 0.1%
141751 1
< 0.1%
141753 1
< 0.1%
141798 1
< 0.1%
198864 1
< 0.1%
198866 1
< 0.1%
200001 1
< 0.1%
200004 1
< 0.1%
200005 1
< 0.1%
200006 1
< 0.1%
ValueCountFrequency (%)
608120680 1
< 0.1%
608120662 1
< 0.1%
608101468 1
< 0.1%
603044763 1
< 0.1%
602913587 2
< 0.1%
602913586 2
< 0.1%
602913585 2
< 0.1%
602913584 2
< 0.1%
602913581 1
< 0.1%
602913580 1
< 0.1%

GEOLOCSTLength
Real number (ℝ)

HIGH CORRELATION 

Distinct68613
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020990581
Minimum9.2797277 × 10-6
Maximum4.4271136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size536.6 KiB
2023-12-12T10:07:27.454669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9.2797277 × 10-6
5-th percentile0.00059783043
Q10.0014754407
median0.0032134412
Q30.0088890085
95-th percentile0.090785221
Maximum4.4271136
Range4.4271043
Interquartile range (IQR)0.0074135678

Descriptive statistics

Standard deviation0.091321291
Coefficient of variation (CV)4.3505843
Kurtosis531.41174
Mean0.020990581
Median Absolute Deviation (MAD)0.0022073317
Skewness17.935057
Sum1441.4652
Variance0.0083395781
MonotonicityNot monotonic
2023-12-12T10:07:27.610500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000359972346 3
 
< 0.1%
0.0003599709388 3
 
< 0.1%
2.095407854 × 10-53
 
< 0.1%
0.001609987266 2
 
< 0.1%
0.0008960017839 2
 
< 0.1%
0.001131034747 2
 
< 0.1%
0.0006340218043 2
 
< 0.1%
0.0009690122772 2
 
< 0.1%
0.0008079726137 2
 
< 0.1%
0.0005470030288 2
 
< 0.1%
Other values (68603) 68649
> 99.9%
ValueCountFrequency (%)
9.279727689 × 10-61
 
< 0.1%
9.418095048 × 10-61
 
< 0.1%
9.433196005 × 10-61
 
< 0.1%
9.495564153 × 10-61
 
< 0.1%
9.495564213 × 10-62
< 0.1%
9.899626207 × 10-61
 
< 0.1%
1.08367737 × 10-51
 
< 0.1%
1.303228564 × 10-51
 
< 0.1%
1.817195024 × 10-51
 
< 0.1%
2.095407854 × 10-53
< 0.1%
ValueCountFrequency (%)
4.427113629 1
< 0.1%
4.234289312 1
< 0.1%
4.177167087 1
< 0.1%
4.142640033 1
< 0.1%
3.385682454 1
< 0.1%
3.305478626 1
< 0.1%
3.033943389 1
< 0.1%
2.952415007 1
< 0.1%
2.928561639 1
< 0.1%
2.866949296 1
< 0.1%

Interactions

2023-12-12T10:07:23.649708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.395105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.885140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.265823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.649709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.111425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.497090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.877429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.270204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.692640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.477219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.931179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.310821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.694350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.160334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.541313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.924317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.314088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.733783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.536029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.971840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.352417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.818381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.202061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.583207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.967847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.355721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.774038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.590401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.012917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.392883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.858359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.245394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.624067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.012114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.397870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.815383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.661387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.053877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.434346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.899736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.287088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.668219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.054912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.440962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.855191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.709176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.097086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.475713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.940834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.328645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.709111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.098923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.483567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.893554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.753496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.138396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.518688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.983793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.369914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.752005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.140950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.524186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.936706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.799709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.183785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.567705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.027460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.415047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.797614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.185985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.568740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.978915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:20.844420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.225314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:21.609713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.070442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.456598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:22.838660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.229134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:07:23.609428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2023-12-12T10:07:27.648993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CWYEND_SLKEND_TRUE_DISTGEOLOCSTLengthLG_NONETWORK_TYPEOBJECTIDRA_NAMERA_NOROUTE_NE_IDSPEED_LIMITSTART_SLKSTART_TRUE_DIST
CWY1.000-0.157-0.155-0.0780.0660.277-0.1820.0590.017-0.0650.265-0.279-0.275
END_SLK-0.1571.0001.0000.9210.3460.3000.1860.1180.097-0.0990.0860.4330.433
END_TRUE_DIST-0.1551.0001.0000.9210.3470.2970.1860.1190.097-0.0990.0860.4320.432
GEOLOCSTLength-0.0780.9210.9211.0000.3140.0630.0870.0690.085-0.1240.0420.1700.169
LG_NO0.0660.3460.3470.3141.0000.0700.2590.8960.183-0.1070.1970.1380.139
NETWORK_TYPE0.2770.3000.2970.0630.0701.0000.3450.0670.0180.2310.6410.5150.510
OBJECTID-0.1820.1860.1860.0870.2590.3451.0000.2420.104-0.0050.4650.3220.321
RA_NAME0.0590.1180.1190.0690.8960.0670.2421.0000.390-0.0000.2320.0430.042
RA_NO0.0170.0970.0970.0850.1830.0180.1040.3901.000-0.0040.2600.0330.032
ROUTE_NE_ID-0.065-0.099-0.099-0.124-0.1070.231-0.005-0.000-0.0041.0000.0550.0630.062
SPEED_LIMIT0.2650.0860.0860.0420.1970.6410.4650.2320.2600.0551.0000.1290.128
START_SLK-0.2790.4330.4320.1700.1380.5150.3220.0430.0330.0630.1291.0000.997
START_TRUE_DIST-0.2750.4330.4320.1690.1390.5100.3210.0420.0320.0620.1280.9971.000

Missing values

2023-12-12T10:07:24.045772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:07:24.172113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OBJECTIDROADROAD_NAMECOMMON_USAGE_NAMESTART_SLKEND_SLKCWYSTART_TRUE_DISTEND_TRUE_DISTNETWORK_TYPERA_NORA_NAMELG_NOLG_NAMESPEED_LIMITROUTE_NE_IDGEOLOCSTLength
0841591112110042Hacket RdHacket Rd0.01.15Single0.01.15Local Road2South West211Harvey50km/h applies in built up areas or 110km/h outside built up areas2182430.010896
1841591122040155Knight StKnight St0.00.86Single0.00.86Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2235940.009268
2841591132040149Timperley RdTimperley Rd0.01.03Single0.01.03Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2424820.011123
3841591142040145Mossop StMossop St0.00.39Single0.00.39Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2296820.003460
4841591152040144Guthrie StGuthrie St0.00.20Single0.00.20Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2182000.001862
5841591162040136Floreat StFloreat St0.00.38Single0.00.38Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2152550.003993
6841591172040132Dunstan StDunstan St0.01.22Single0.01.22Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2129500.011666
7841591182040129Glenroy StGlenroy St0.00.24Single0.00.24Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2170050.002672
8841591192040126West RdWest Rd0.00.33Single0.00.33Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2455100.003445
9841591202040114Jarvis StJarvis St0.00.69Single0.00.69Local Road2South West204Bunbury (C)50km/h applies in built up areas or 110km/h outside built up areas2217500.006389
OBJECTIDROADROAD_NAMECOMMON_USAGE_NAMESTART_SLKEND_SLKCWYSTART_TRUE_DISTEND_TRUE_DISTNETWORK_TYPERA_NORA_NAMELG_NOLG_NAMESPEED_LIMITROUTE_NE_IDGEOLOCSTLength
68662842277734240022Dulbelling South RdDulbelling South Rd1.982.02Single1.982.02Local Road8Wheatbelt424Quairading110km/h2127930.000373
68663842277744240022Dulbelling South RdDulbelling South Rd5.755.78Single5.755.78Local Road8Wheatbelt424Quairading110km/h2127930.000273
68664842277754240005Cubbine RdCubbine Rd0.0020.88Single0.0020.88Local Road8Wheatbelt424Quairading110km/h2106700.217490
68665842277764240005Cubbine RdCubbine Rd35.9040.16Single35.9040.16Local Road8Wheatbelt424Quairading110km/h2106700.043219
68666842277774240105Andrews RdAndrews Rd5.675.71Single5.675.71Local Road8Wheatbelt424Quairading110km/h2011210.000424
68667842277784240037Bland RdBland Rd4.084.14Single4.084.14Local Road8Wheatbelt424Quairading110km/h2043190.000562
68668842277794240037Bland RdBland Rd4.794.82Single4.794.82Local Road8Wheatbelt424Quairading110km/h2043190.000282
68669842277804240023Dangin South RdDangin South Rd14.2814.30Single14.2814.30Local Road8Wheatbelt424Quairading110km/h2111910.000188
68670842277814240015Hayes RdHayes Rd0.0014.24Single0.0014.24Local Road8Wheatbelt424Quairading110km/h2192410.132928
68671842277824240026Carter - Doodenanning RdCarter - Doodenanning Rd4.228.11Single4.228.11Local Road8Wheatbelt424Quairading110km/h2074680.036484